Loading...
Comparative analysis of contextual and context-free embeddings in disaster prediction from Twitter data
Deb, Sumona
Deb, Sumona
Citations
Altmetric:
Genre
Journal article
Date
2022-02-02
Advisor
Committee member
Group
Department
Computer and Information Sciences
Permanent link to this record
Collections
Research Projects
Organizational Units
Journal Issue
DOI
http://dx.doi.org/10.1016/j.mlwa.2022.100253
Abstract
Twitter is a social media site where people post their personal experiences, opinions, and news. Due to the ubiquitous real-time data availability, many rescue agencies monitor this data regularly to identify disasters, reduce risk, and save lives. However, it is impossible for humans to manually check the mass amount of data and identify disasters in real-time. For this purpose, many research have been proposed to present words in machine-understandable representations and apply machine learning methods on the word representations to identify the sentiment of a text. The previous research methods provide a single vector representation or embedding of a word from a given document. However, the recent advanced contextual embedding method (BERT — Bidirectional Encoder Representations from Transformers) constructs different vectors for the same word in different contexts. The BERT embeddings have been used successfully in various Natural Language Processing (NLP) tasks, yet there is no concrete analysis of how these representations are helpful in disaster-type tweet analysis. This research study explores the efficacy of the BERT embeddings on predicting disaster from Twitter data and compares these to traditional context-free word embedding methods. We provide both quantitative and qualitative results for this study. The results show that the contextual embeddings have the best results in disaster prediction task than the traditional word embeddings. Furthermore, we discuss the opportunities and challenges of contextual embeddings on sentiment analysis of Twitter data.
Description
Citation
Sumona Deb, Ashis Kumar Chanda, Comparative analysis of contextual and context-free embeddings in disaster prediction from Twitter data, Machine Learning with Applications, Volume 7, 2022, 100253, ISSN 2666-8270, https://doi.org/10.1016/j.mlwa.2022.100253.
Citation to related work
Elsevier
Has part
Machine Learning with Applications, Vol. 7
ADA compliance
For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu